Hand gesture recognition by histogram based kernel using density measure

This paper presents the recognition of hand gesture in the research field of machine vision. Vision based hand gesture recognition has the capacity to develop a tool for Human Machine Interaction (HCI). The automated threshold methods were used as pre-processing steps for extraction of feature vector using chain code histogram (CCH). Then, construct the kernel based on histogram of chain code using density measure to obtain discriminative feature descriptor for efficient recognition of hand gesture using Support Vector Machine (SVM). Cluster based threshold techniques involves Otsu thtresholding (OT), Ridler and Calvard thresholding (RCT), and Kittler and Illingworth thresholding (KIT) are used to segment the region of interest for feature extraction. In this paper, CCH based on various segmentation methods were compared to measure the recognition rate by SVM classifier. The proposed RCT-CCH based kernel method increase the recognition rate of hand posture by 90%, compared with cluster based thresholds.